We are currently helping a client in the insurance sector expand its AI team, and the search has highlighted a role that is absolutely critical for any successful AI initiative: the Data Developer.
Too often, data scientists are forced to spend the majority of their time on data wrangling—ingesting, cleaning, and preparing data—rather than on advanced analysis and model building. A dedicated Data Developer solves this problem. They are responsible for the entire data lifecycle, building the robust pipelines that ensure clean, structured data is ready for the data science team to work their magic.
For a recent opening, we sought a professional with 2-3 years of experience in this specific function. The ideal candidate has a strong foundation in technologies and tasks such as:
- SQL and relational databases (RDBMS)
- BI, ETL, and DWH development
- Building and configuring data streaming and data pipelines
- Data cleansing and Python programming
- A conceptual understanding of Machine Learning
This position not only provides a solid data foundation for the organization but also offers the developer a practical path into the world of Data Science, with hands-on experience in building end-to-end AI systems. It’s a powerful reminder that building a great AI team is a strategic effort that starts with solid data engineering.